Neda Bagheri

The paper investigates how altering cell size asymmetry in Drosophila neural stem cells affects their proliferation and lineage potential, finding increased stem cell pools but reduced lineage size and differentiation.

The paper develops and optimizes a one-pot NASBA-Cas13a assay for rapid, sensitive detection of SARS-CoV-2 RNA without specialized equipment.

The paper develops a hybrid agent-based model to simulate hiPS cell colony dynamics, identifies limitations of the CPM approach, and suggests improvements for accurate simulations.

The paper improves machine learning emulation of a tumor microenvironment model by incorporating temporal information from intermediate simulation states, enhancing predictive performance.

The paper investigates how energy metabolism affects the regulatory impact of activators on gene expression using a mathematical model and experimental validation in Drosophila.

The paper shows that a two-fold change in the Pnt/Yan transcription factor ratio drives photoreceptor fate transitions in Drosophila eye progenitor cells, revealing a mechanism for ultrasensitivity.

The paper introduces GAMES, a workflow for developing dynamic models of synthetic genetic systems, demonstrating it with a case study on a chemically responsive transcription factor.

This paper presents a method for designing mammalian genetic programs using predictive models and modular genetic parts to achieve complex cellular functions.

Fly-QMA automates the analysis of mosaic imaginal discs in Drosophila by segmenting cells, annotating clones, and quantifying gene expression changes.

The paper shows macrophages use quorum licensing, a density-dependent mechanism, to regulate collective activation in response to LPS, leading to bimodal phenotypic partitioning.

COMET provides a toolkit of transcription factors and promoters to design and tune gene expression in mammalian cells with customizable genetic programs.

The paper shows that layered weak repression synchronizes developmental gene regulation with cellular metabolism, and that reducing metabolism can compensate for loss of repressors.

DUETT identifies RNA structural transitions from chemical probing data by using a feedback control-inspired method and linear regression to detect known and novel events.